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This course, Applied Artificial Intelligence with DeepLearning, is part of the IBM Advanced Data Science Certificate which IBM is currently creating and gives you easy access to the invaluable insights into Deep Learning models used by experts in Natural Language Processing, Computer Vision, Time Series Analysis, and many other disciplines. We’ll learn about the fundamentals of Linear Algebra and Neural Networks. Then we introduce the most popular DeepLearning Frameworks like Keras, TensorFlow, PyTorch, DeepLearning4J and Apache SystemML. Keras and TensorFlow are making up the greatest portion of this course. We learn about Anomaly Detection, Time Series Forecasting, Image Recognition and Natural Language Processing by building up models using Keras on real-life examples from IoT (Internet of Things), Financial Marked Data, Literature or Image Databases. Finally, we learn how to scale those artificial brains using Kubernetes, Apache Spark and GPUs.
IMPORTANT: THIS COURSE ALONE IS NOT SUFFICIENT TO OBTAIN THE "IBM Watson IoT Certified Data Scientist certificate". You need to take three other courses where two of them are currently built. The Specialization will be ready late spring, early summer 2018
Using these approaches, no matter what your skill levels in topics you would like to master, you can change your thinking and change your life. If you’re already an expert, this peep under the mental hood will give your ideas for turbocharging successful creation and deployment of DeepLearning models. If you’re struggling, you’ll see a structured treasure trove of practical techniques that walk you through what you need to do to get on track. If you’ve ever wanted to become better at anything, this course will help serve as your guide.
Prerequisites: Some coding skills are necessary. Preferably python, but any other programming language will do fine. Also some basic understanding of math (linear algebra) is a plus, but we will cover that part in the first week as well.
If you choose to take this course and earn the Coursera course certificate, you will also earn an IBM digital badge. To find out more about IBM digital badges follow the link ibm.biz/badging.

教學方

Romeo Kienzler

Chief Data Scientist, Course Lead

Niketan Pansare

Senior Software Engineer

Tom Hanlon

Training Director

Max Pumperla

Deep Learning Engineer

Ilja Rasin

Data Scientist

腳本

LSTMs are so powerful that we can dedicate a whole lecture on how they are working. You could take an entire course on LSTMs, and if you are planning to do so please check out the description of this video. But we will try to give you a more intuitive way of looking at LSTMs. So this is a single neuron in LSTMs. By the way, LSTMs stands for Long Short Term Memory Networks. And s in the feet forward network, it marks an input xt, to an output vector, ht, by using weights and an activation function. Note that the same holds whether you are using scalars or vectors as input and output. But we now see a lot of additional components. So the first thing we notice is that there is no direct connection between xt and ht. All data flows through ct, which is the so called cell state. Cell state is the actual memory of the LSTM neuron. Notice that there are three additional units present in a LSTM neuron, an input gate, an output gate, and a forget gate. Those three gates are controlling the state of ct. The way how this is controlled is as follows. So have a look at the input first. The first thing we notice is that xt is not only used as input of the neuron but also as input to the gate. So the input gate, as the other gates, has a separate weight vector which is straight from the input data and learns to control the influx of information into the cell's data city. This is done by a vector dot product between the input xt after it has been squashed by the activation function and the output of the input gate. In other words, through the weight vector of the input gate the neuron can learn from creating data. When it is a good idea to open the gate and have the input start in the cell. Often it is a bad idea to remember things and close the influx information into the cell state ct. Note that this is a continuous value, so just like a wall which can be partially opened and closed. Finally it is important to notice that all the cell state has an influence on the gate. This is again accomplished through a separate weight vector so that the actual input gate is controlled by the historic cell state as well as by the actual value of xt. So now let's have a look at the output gate. Again, it is controlled by the actual value xt and by the actual cell state ct. Here the output gate controls how much of cell state ct gets output to down stimulants connected to ht. So this topology is the initial LSTM proposed by and Hoover in 1997. In 1999 Felix and added an additional component, the forget gate. They discovered that without the capability of forgetting the cell state ct may grow indefinitely and eventually causes the network to break down. Again the forget gate is controlled by the actual input xt and the current cell state ct. And again through calculation of induct product between the output of the forget gate and the previous cell state ct, it controls how much of the actual cell state ct is preserved. Another exotic but totally exciting neronetwork technology is an autoencoder. So let's learn about it in the next lecture.